Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "237" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 59 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 57 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459874 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 7.898943 | 5.256603 | 0.554631 | 8.191408 | 4.132207 | 5.349145 | 0.539234 | -1.052071 | 0.6326 | 0.6344 | 0.3968 | nan | nan |
| 2459873 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459872 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.386578 | 3.400451 | 1.804017 | 14.885738 | 5.134501 | 4.667869 | -0.010244 | -0.901469 | 0.6221 | 0.6280 | 0.4088 | nan | nan |
| 2459871 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.796408 | 2.752685 | 1.187645 | 15.606753 | 4.602088 | 4.955164 | -0.182428 | -0.999017 | 0.6357 | 0.6329 | 0.4004 | nan | nan |
| 2459870 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 6.736543 | 4.465137 | 0.973047 | 11.435460 | 3.544691 | 2.980558 | 1.639390 | -0.476910 | 0.6431 | 0.6402 | 0.4025 | nan | nan |
| 2459869 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.289273 | 3.263948 | 0.943607 | 11.398616 | 2.842305 | 2.565647 | 1.174745 | 0.156602 | 0.6521 | 0.6585 | 0.3990 | nan | nan |
| 2459868 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 6.670616 | 4.671025 | 0.967685 | 17.610219 | 2.931206 | 3.096917 | 0.154475 | -1.269698 | 0.6247 | 0.6290 | 0.4156 | nan | nan |
| 2459867 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.279937 | 3.063574 | 1.083620 | 13.820125 | 2.054573 | 2.057572 | 0.145226 | -1.213844 | 0.6392 | 0.6363 | 0.4156 | nan | nan |
| 2459866 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.913495 | 3.399232 | 1.215652 | 12.762209 | 3.307453 | 2.049503 | -0.193561 | -0.916805 | 0.6428 | 0.6395 | 0.4082 | nan | nan |
| 2459865 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 7.391073 | 5.542760 | 1.355147 | 16.213611 | 4.891083 | 2.883292 | 2.090433 | 2.706877 | 0.6546 | 0.6510 | 0.3851 | nan | nan |
| 2459864 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459863 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 3.356348 | 2.140805 | -1.648849 | -1.053190 | 0.450922 | -0.901545 | -0.491040 | -0.940352 | 0.6314 | 0.6245 | 0.4164 | nan | nan |
| 2459862 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.616254 | 2.681207 | 3.872526 | 10.625960 | 2.684535 | 2.284920 | -0.244498 | -0.847027 | 0.6042 | 0.6431 | 0.4401 | nan | nan |
| 2459861 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.956933 | 1.131304 | -1.576989 | -2.238615 | -0.644411 | -1.539360 | -0.083993 | -0.618875 | 0.6469 | 0.6324 | 0.4299 | nan | nan |
| 2459860 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.162016 | 1.330334 | 3.117686 | 8.607397 | 3.042920 | 3.828282 | 1.105612 | -0.761514 | 0.6596 | 0.6356 | 0.4258 | nan | nan |
| 2459859 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.322867 | 0.870623 | -1.671637 | -2.098700 | -0.909379 | -1.447585 | 0.355979 | -0.766354 | 0.6676 | 0.6424 | 0.4198 | nan | nan |
| 2459858 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.668388 | 0.789859 | -1.750999 | -2.615773 | -0.621221 | -1.738691 | 1.723462 | -0.217009 | 0.6719 | 0.6460 | 0.4343 | 2.729064 | 2.593831 |
| 2459857 | RF_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 2.163038 | -0.460607 | 1.469996 | 1.518458 | -1.357776 | -0.597547 | -1.407024 | -1.311896 | 0.0288 | 0.0289 | 0.0005 | nan | nan |
| 2459856 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.865133 | 2.134213 | 3.107943 | 8.161401 | 1.193103 | 1.377603 | -0.003448 | -1.496432 | 0.6692 | 0.6614 | 0.4164 | 2.689488 | 2.582426 |
| 2459855 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 4.511279 | 3.058827 | 3.407170 | 9.060482 | 0.508962 | 0.685470 | 0.764252 | -0.377673 | 0.6458 | 0.6827 | 0.4487 | 2.482551 | 2.486815 |
| 2459854 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 4.026828 | 2.507566 | 3.658221 | 8.046999 | -0.117979 | 0.097675 | 1.306276 | 0.278340 | 0.6655 | 0.7171 | 0.4623 | 2.885786 | 2.645296 |
| 2459853 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.601765 | 1.919405 | 4.749562 | 10.394968 | 1.625084 | 1.676209 | 1.062679 | -1.007863 | 0.6956 | 0.6542 | 0.4433 | 2.898507 | 2.709752 |
| 2459852 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459851 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459850 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.865844 | 2.768592 | 4.646030 | 9.535119 | 1.766854 | 3.739859 | 1.168086 | 3.479271 | 0.6966 | 0.7224 | 0.3685 | 2.626961 | 2.600104 |
| 2459849 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.927141 | 2.123867 | 10.532598 | 20.513865 | 0.564011 | 1.649285 | 1.061828 | -1.296032 | 0.6987 | 0.7160 | 0.3742 | 3.469490 | 3.159442 |
| 2459848 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 3.345642 | 2.515883 | 7.785510 | 14.251567 | 1.918947 | 4.203937 | 0.153203 | -1.193097 | 0.6703 | 0.7166 | 0.3947 | 2.884272 | 2.920087 |
| 2459847 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 3.780555 | 2.603812 | 7.185865 | 13.299118 | 1.721912 | 1.105892 | 0.209000 | -0.853115 | 0.6735 | 0.6449 | 0.4517 | 5.429945 | 4.158871 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | nn Power | 8.191408 | 7.898943 | 5.256603 | 0.554631 | 8.191408 | 4.132207 | 5.349145 | 0.539234 | -1.052071 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | nn Power | 14.885738 | 3.400451 | 5.386578 | 14.885738 | 1.804017 | 4.667869 | 5.134501 | -0.901469 | -0.010244 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | nn Power | 15.606753 | 2.752685 | 3.796408 | 15.606753 | 1.187645 | 4.955164 | 4.602088 | -0.999017 | -0.182428 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | nn Power | 11.435460 | 6.736543 | 4.465137 | 0.973047 | 11.435460 | 3.544691 | 2.980558 | 1.639390 | -0.476910 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | nn Power | 11.398616 | 5.289273 | 3.263948 | 0.943607 | 11.398616 | 2.842305 | 2.565647 | 1.174745 | 0.156602 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | nn Power | 17.610219 | 6.670616 | 4.671025 | 0.967685 | 17.610219 | 2.931206 | 3.096917 | 0.154475 | -1.269698 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | nn Power | 13.820125 | 4.279937 | 3.063574 | 1.083620 | 13.820125 | 2.054573 | 2.057572 | 0.145226 | -1.213844 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | nn Power | 12.762209 | 3.399232 | 4.913495 | 12.762209 | 1.215652 | 2.049503 | 3.307453 | -0.916805 | -0.193561 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | nn Power | 16.213611 | 7.391073 | 5.542760 | 1.355147 | 16.213611 | 4.891083 | 2.883292 | 2.090433 | 2.706877 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | ee Shape | 3.356348 | 3.356348 | 2.140805 | -1.648849 | -1.053190 | 0.450922 | -0.901545 | -0.491040 | -0.940352 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | nn Power | 10.625960 | 3.616254 | 2.681207 | 3.872526 | 10.625960 | 2.684535 | 2.284920 | -0.244498 | -0.847027 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | ee Shape | 1.956933 | 1.131304 | 1.956933 | -2.238615 | -1.576989 | -1.539360 | -0.644411 | -0.618875 | -0.083993 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | nn Power | 8.607397 | 2.162016 | 1.330334 | 3.117686 | 8.607397 | 3.042920 | 3.828282 | 1.105612 | -0.761514 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | ee Shape | 1.322867 | 1.322867 | 0.870623 | -1.671637 | -2.098700 | -0.909379 | -1.447585 | 0.355979 | -0.766354 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | ee Temporal Discontinuties | 1.723462 | 0.789859 | 1.668388 | -2.615773 | -1.750999 | -1.738691 | -0.621221 | -0.217009 | 1.723462 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | ee Shape | 2.163038 | -0.460607 | 2.163038 | 1.518458 | 1.469996 | -0.597547 | -1.357776 | -1.311896 | -1.407024 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | nn Power | 8.161401 | 2.865133 | 2.134213 | 3.107943 | 8.161401 | 1.193103 | 1.377603 | -0.003448 | -1.496432 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | nn Power | 9.060482 | 3.058827 | 4.511279 | 9.060482 | 3.407170 | 0.685470 | 0.508962 | -0.377673 | 0.764252 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | nn Power | 8.046999 | 2.507566 | 4.026828 | 8.046999 | 3.658221 | 0.097675 | -0.117979 | 0.278340 | 1.306276 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | nn Power | 10.394968 | 1.919405 | 2.601765 | 10.394968 | 4.749562 | 1.676209 | 1.625084 | -1.007863 | 1.062679 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | nn Power | 9.535119 | 2.865844 | 2.768592 | 4.646030 | 9.535119 | 1.766854 | 3.739859 | 1.168086 | 3.479271 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | nn Power | 20.513865 | 2.927141 | 2.123867 | 10.532598 | 20.513865 | 0.564011 | 1.649285 | 1.061828 | -1.296032 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | nn Power | 14.251567 | 2.515883 | 3.345642 | 14.251567 | 7.785510 | 4.203937 | 1.918947 | -1.193097 | 0.153203 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 237 | N18 | RF_ok | nn Power | 13.299118 | 2.603812 | 3.780555 | 13.299118 | 7.185865 | 1.105892 | 1.721912 | -0.853115 | 0.209000 |